Prior art analyzers have combined multiple gas chromatography (GC) columns comprising non-polar and polar stationary-phase materials and also a mass spectrometer (MS). However, the mass spectrometer destroys (by fragmenting) the molecules that it analyzes. Thus, the output of the MS cannot be coupled back into another GC column for additional separation. The MS can distinguish between the components in some mixtures, but depends on the GC columns to do most of the separation of the components in complex mixtures. Other prior analyzers have combined a GC column and an infrared absorption spectrometer (IAS), with the output of the GC column coupled to a gas-carrying tube in the IAS. Mid-infrared light is coupled to this tube and certain wavenumbers of that light is absorbed by the molecules conducted through the tube. The pattern of absorbed and non-absorbed wavenumbers of light is detected by a photo-detector in the IAS. This pattern of absorbed and non-absorbed wavenumbers represents an absorption spectrum of the gas in the tube. In some of these prior GC-IAS analyzers, the output of the IAS tube could be coupled to another chemical detector, such as a thermal-conductivity detector, that senses the presence of a compound in the gas flow, since that compound to be detected generally is a larger molecule than the molecules of the carrier gas for the flow, which typically would be helium or nitrogen. For all of these prior analyzers, either GC-MS or GC-IAS, the gas mixture first goes through the set of GC columns and is supplied to the spectrometer but is not coupled again to additional GC columns.
Examples of prior art gas analyzers based on infrared-laser absorption spectroscopy include the LaserSenseML™ system by Block Engineering, the Nitrolux™ ammonia sensor by Pranalytica and the Mini Monitor by Aerodyne Research, Inc. The LaserSense™ system measures various hydrocarbon gases (C1 through C5). The systems from Aerodyne Research can detect and monitor trace gases such as methane, nitrous oxide, nitric oxide, carbon monoxide, carbon dioxide, formaldehyde, formic acid, ethylene, acetylene, carbonyl sulfide, acrolein, ammonia and others. These gas analyzers contain a quantum-cascade (QC) laser source of tunable wavelength (or wavenumber), one or more gas-containing cells, and a photodetector. Light of only one wavenumber is transmitted at a given time from the QC laser and the single-wavelength light is directed onto the gas molecules, which may be in a multi-pass optical cell, to increase the interaction distance of the light with the molecules being characterized. Light exiting the gas cell is then collected and detected by a photodetector in the system.
Published articles describe the use of tunable quantum cascade laser spectroscopy to detect nitro-aromatic and peroxide explosives in air (see L. C. Pacheco-Londono et al., “Detection of nitroaromatic and peroxide explosives in air using infrared spectroscopy: QCL and FTIR,” Advances in Optical Technologies, v. 2013, article 532670 (2013)) and to detect fluorocarbons (see M. C. Phillips, et al., “Real-time trace gas sensing of fluorocarbons using a swept-wavelength external cavity quantum cascade laser,” Analyst, v. 139, p. 2047 (2014)). These articles report demonstrations of sensing of chemicals at low part-per-billion (ppb) concentration levels. Although excellent sensitivities have been achieved by the QC laser based gas analyzers, they have not been able to handle complex mixtures of gases. The most complex mixtures analyzed have contained four or five target species (see M. C. Phillips, et al supra and R. E. Baren, et al., “Quad quantum cascade laser spectrometer with dual gas cells for the simultaneous analysis of mainstream and sidestream cigarette smoke,” Spectrochimica Acta, Part A, v. 60, p. 3437 (2004)). Algorithms such as partial least squares discriminant analysis and principle component analysis were used to analyze the measured infrared absorption spectra (see L. C. Pacheco-Londono et al. and M. C. Phillips, et al. supra).
Some gas chromatography systems provide separation of mixtures and typically use a detection method such as a flame ionization detector, a photo-ionization detector, or a thermal conductivity detector to sense the presence of analyte molecules that are carried in a flow of the carrier gas, which typically is helium or nitrogen. In general, these detectors do not provide discrimination between chemicals and thus the GC column provides all the necessary separation of mixtures into the individual chemical species or components. Some prior GC systems use as its detector arrays of multiple transducers coated with various chemically selective absorptive films. An example of such prior micro-scale GC systems was able to distinguish mixtures of more than 20 species by using an array of detectors such as chemi-resistors that have chemically selective coatings (see W. R. Collin, et al., “Microfabricated gas chromatograph for rapid, trace-level determinations of gas-phase explosive marker compounds,” Analytical Chemistry, v. 86, p. 655 (2013)). However, these transducer arrays are not able to handle mixtures containing more than two or three species that are output simultaneously from a GC column, and thus are not physically separated. Even for these relatively simple mixtures, the error in recognition of a component species in the mixture was 5% and higher and maximum concentration ratio that could be handled was only 20:1 (see C. Jin and E. T. Zellers, “Limits of recognition for binary and ternary vapor mixtures determined with multitransducer arrays,” Analytical Chemistry, v. 80, p. 7283 (May 2008)).
Systems that combine gas chromatography and mass spectrometry can have better specificity, can handle mixtures with more components and also can handle a larger dynamic range, or abundance sensitivity, of relative concentration levels (see N. Ragunathan, et al., “Gas chromatography with spectroscopic detectors,” J. Chromatography A, v. 856, p. 3 49 (1999)). However, the mass spectrometers require high-vacuum pumps and generally are bulky and consume much electrical power. Also, these systems may require use of He or H2 carrier gas.
Some prior analyzers have the output of a GC column coupled to an optical spectrometer that measured the absorption spectrum of the molecules in that output gas flow (see N. Ragunathan, et al., supra; S. Mengali, et al., “Rapid screening and identification of illicit drugs by IR absorption spectroscopy and gas chromatography,” Proceedings of SPIE Vol. 8631, p. 86312F (2013); and S. Wu, et al., “Hollow waveguide quantum cascade laser spectrometer as an online microliter sensor for gas chromatography,” Journal of Chromatography A, v. 1188, p. 327 (2008)).
More recent versions of these GC-IAS systems comprise a wavelength scanned quantum-cascade laser whose emitted light is coupled into a long hollow tube in which the gas to be characterized flows (see N. Ragunathan, et al. and S. Mengali, et al., supra). The combination of the relatively high laser power at each wavelength and the long interaction distance provide by the hollow-waveguide tube enables these GC-IAS systems to approach the sensitivity of the GC-MS systems. These GC-IAS systems have been used only to detect or screen for specific analyte compounds and have not been used for analyzing the constituents in complex mixtures.
Thus, there is a need for gas analyzers that are compact and have low power consumption as well as can handle complex mixtures of many gas species with a large dynamic range of concentration levels.
The absorption spectra measured by prior art GC-IAS systems have not been used to provide additional de-mixing or separation of the components in a gas mixture. The combination of infrared spectrometry and the Independent Component Analysis (ICA) and Sparse Reconstruction and Classification (SRC) algorithms for de-mixing and chemical identification by spectral analysis is described in U.S. Provisional Patent Application Ser. No. 62/234,653, filed in the United States on Sep. 29, 2015, entitled, “Fusion of Independent Component Analysis and Sparse Representation and Classification for Analysis of Spectral Data,” and in a related application which claims the benefit of 62/234,653, namely U.S. patent application Ser. No. 15/280,575 filed Sep. 29, 2016 and entitled “Fusion of Independent Component Analysis and Sparse Representation Based Classification for Analysis of Spectral Data”. That ICA makes use of multiple spectra measured at differing relative concentrations of the component species in a mixture is ideally suited for the analysis of incompletely separated mixtures that are output or eluted from a gas chromatography column. There is a prior art example in which ICA has been applied to optical spectra (see Y. Sun, et al., “A semi-blind source separation method for differential optical absorption spectroscopy of atmospheric gas mixtures,” Inverse Problems and Imaging, v. 8, p. 87 (2014)). In that case, ICA was used after a least-squared fitting algorithm was performed first to identify the known components in a mixture. ICA was applied then to the remainder matrix generated by the fitting algorithm to extract the remnant gas components. ICA was not used, in this prior art, to generate de-mixed spectra from the measured spectra of a gas mixture so that the identification algorithm could be more effective, as is done in the presently disclosed analyzer.
Independent Component Analysis (ICA) is a known algorithm for separating a set of mixtures of signals into the constituent components by optimizing a measure of the statistical independence of the outputs. It relies on the components being statistically independent, but does not use prior knowledge of the signals (i.e., it operates blindly). Sparse Representation-based Classification (SRC) models a multi-dimensional signal as a sparse mixture of known library elements by maximizing the sparsity of representation while maintaining the fidelity of the mixture model. These library elements depend on the nature of the signals. For infrared (IR) spectroscopy, they are examples of the chemical spectra of individual substances. SRC also models possible deformations of the signal that can occur during the measurement process. Since ICA and SRC utilize almost orthogonal types of information, using ICA as a front-end for SRC results in a very low net false alarm rate that is close to the product of the individual false alarm rates for ICA and SRC. The ICA-SRC combination also separates spectra from different materials, such as, for example, explosives and commonly occurring surface materials such as plastics, and noise before final classification is performed, which greatly increases clutter rejection and increases sensitivity (e.g., the minimum detectable concentration of a substance), because of the increased signal to noise ratio.
ICA and SRC were developed for different applications. ICA is used primarily for analysis of one-dimensional (1-D) signals, such as audio mixtures or spectra, and also for some image processing applications. SRC, however, was developed by workers in computer vision for recognizing structured two-dimensional (2D) images, such as faces, in a robust way that can compensate for variabilities due to changes in illumination or pose
There is prior art relating to gas chromatography systems comprising multiple stages of GC columns that have an on-column detector place at the end of the first GC column (see J. Liu, et al., “Smart multi-channel two-dimensional micro-gas chromatography for rapid workspace hazardous volatile organic compounds measurement,” Lab Chip, v. 13, p. 818 (2013) and J. Liu, et al., “Adaptive two-dimensional microgas chromatography,” Analytical Chemistry, v. 184, p. 4214 (2012)). The on-column detectors of this prior only sense the presence of a gas analyte and has very limited ability to distinguish between different analyte molecules, in contrast to the disclosed analyzer which has a spectroscopic detector. The information output from this on-column detector is used to route the gas flow from the first GC column to multiple second GC columns. However, this information is based more on the occurrence of an analyte detection event rather than on chemical-specific characteristics, such as absorption spectrum, of the constituents in the detected pulse of analyte mixture from the first GC column. Thus this prior approach would distinguish only poorly whether that detected pulse comprises a mixture of many co-eluding compounds, only several co-eluding compounds or a single compound. The kind of control implemented in the prior systems only select among several second GC columns based on which of those GC column is already being used to perform a separation. In contrast to this prior analyzer, the control in some embodiments of the present analyzer is based on the estimated contents of the output of the first GC column, to select specific portions of the output from the first GC column to undergo additional separation by a second GC column. The inventors are not aware of any prior instruments based on combining gas chromatography and infrared absorption spectrometry that re-circulate the gas-phase output from the optical spectrometer to another mixture-separation column of the gas chromatograph, in accordance with some embodiments.